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Markov Chain Monte Carlo

143 papers

Papers per year

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Papers

Probabilistic Permutation Synchronization Using the Riemannian Structure of the Birkhoff Polytope CVPR 2019
The promises and pitfalls of Stochastic Gradient Langevin Dynamics NIPS 2018
Large-Scale Stochastic Sampling from the Probability Simplex NIPS 2018
HOGWILD!-Gibbs can be PanAccurate NIPS 2018
Mental Sampling in Multimodal Representations NIPS 2018
Dimensionally Tight Bounds for Second-Order Hamiltonian Monte Carlo NIPS 2018
Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks NIPS 2017
A-NICE-MC: Adversarial Training for MCMC NIPS 2017
Collapsed variational Bayes for Markov jump processes NIPS 2017
Stochastic Gradient Monomial Gamma Sampler ICML 2017
Measuring the reliability of MCMC inference with bidirectional Monte Carlo NIPS 2016
Synthesis of MCMC and Belief Propagation NIPS 2016
Towards Unifying Hamiltonian Monte Carlo and Slice Sampling NIPS 2016
Stochastic Gradient Geodesic MCMC Methods NIPS 2016
Stochastic Gradient MCMC with Stale Gradients NIPS 2016
Learning Weight Uncertainty With Stochastic Gradient MCMC for Shape Classification CVPR 2016
Stochastic Quasi-Newton Langevin Monte Carlo ICML 2016
Tree-Guided MCMC Inference for Normalized Random Measure Mixture Models NIPS 2015
Measuring Sample Quality with Stein's Method NIPS 2015
MCMC for Variationally Sparse Gaussian Processes NIPS 2015
Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling NIPS 2015
Reflection, Refraction, and Hamiltonian Monte Carlo NIPS 2015
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families NIPS 2015
A Complete Recipe for Stochastic Gradient MCMC NIPS 2015
On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators NIPS 2015